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This study aims to examine the effects of physical attraction, social attraction and task attraction, which are interpersonal attraction components of service staff, on interactional justice, procedural justice and distributive justice, all of which are components of service justice.

This study was conducted after deriving measurement tools through two preliminary studies. The research sample was made by those who have visited the restaurant where the service staff provides services directly to the customers. Respondents were instructed by investigators to complete the questionnaire based on their most recent visit to the most visited restaurants in the past three months. They received a $5 gift voucher after completing the questionnaire.

Physical attraction negatively affected interactional justice, procedural justice and distributive justice. Social attraction had a negative impact on procedural justice. Task attraction had the greatest positive impact on all service justice factors.

To attract customers’ positive perceptions of service execution and outcomes, task attraction should be considered first rather than physical attraction and social attraction of service staff.

This study expanded the scope of research on interpersonal attraction by studying physical attraction, social attraction and task attraction as interpersonal attraction factors at service encounters, and on service justice by setting the interpersonal attraction as a variable affecting service justice.

The purpose of this paper is to develop a comprehensive research model that can explain customers’ continuance intentions to adopt and use intelligent personal assistants (IPAs).

This study proposes and validates a new theoretical model that extends the parasocial relationship (PSR) theory. Partial least squares analysis is employed to test the research model and corresponding hypotheses on data collected from 304 survey samples.

Interpersonal attraction (task attraction, social attraction, and physical attraction) and security/privacy risk are important factors affecting the adoption of IPAs.

First, this is the first empirical study to examine user acceptance of IPAs. Second, to the authors’ knowledge, no research has been conducted to test the role of PSR in the context of IPAs. Third, this study verified the robustness of the proposed model by introducing new antecedents reflecting risk-related attributes, which has not been investigated in prior PSR research. But this study has limitations that future research may address. First, key findings of this research are based only on data from users in the USA. Second, individual differences among the survey respondents were not examined.

To increase the adoption of IPAs, manufacturers should focus on developing “human-like” and “professional” assistants, in consideration of the important role of PSR and task attraction. R&D should continuously strive to realize artificial intelligence technology advances so that IPAs can better recognize the user’s voice and speak naturally like a person. Collaboration with third-party companies or individual developers is essential in this field, as manufacturers are unable to independently develop applications that support the specific tasks of various industries. It is also necessary to enhance IPA device design and its user interface to enhance physical attraction.

This study is the first empirical attempt to examine user acceptance of IPAs, as most of the prior literature has concerned analysis of usage patterns or technical features.

With the continuous improvement of artificial intelligence (AI) technology, intelligent personal assistants (IPAs) based on AI have seen unprecedented growth. The present study investigates the effect of anthropomorphism on cognitive and emotional trust and the role of interpersonal attraction in the relationship between anthropomorphism and trust.

A structural equation modeling technique with a sample of 263 consumers was used to analyze the data and test the conceptual model.

The findings illustrate that the anthropomorphism of IPAs did not directly induce trust. Anthropomorphism led users to assign greater social attraction and task attraction to IPAs, which in turn reinforced cognitive or emotional trust in these assistants. Compared with task attraction, social attraction was more powerful in strengthening both cognitive trust and emotional trust. The present study broadens the current knowledge about interpersonal attraction and its role in AI usage by examining two types of interpersonal attraction of IPAs.

As trust plays an important role in the rapid development of human–computer interaction, it is imperative to understand how consumers perceive these intelligent agents and build or improve trust. Prior studies focused on the impact of anthropomorphism on overall trust in AI, and its underlying mechanism was underexplored. The findings can help marketers and designers better understand how to enhance users' trust in their anthropomorphic products, especially by increasing social interactive elements or promoting communication.

Chatbot users’ communication experience with disembodied conversational agents was compared with instant messaging (IM) users’ communication experience with human conversational agents. The purpose of this paper is to identify what affects users’ intention to reuse and whether they perceive any difference between the two.

A conceptual model was developed based on computer-mediated communication (CMC) and interpersonal communication theories. Data were collected online from four different continents (North America, Europe, Asia and Australia). Partial least squares structural equation modeling was applied to examine the research model.

The findings mainly reveal that media richness and social presence positively influence trust and reuse intention through task attraction and social attraction; IM users reported significantly higher scores in terms of communication experience, perceived attractiveness of the conversational agent, and trust than chatbot users; users’ trust in the conversational agents is mainly determined by perceived task attraction.

Customers’ evaluation of the communication environment is positively related to their perceived competence of the conversational agent which ultimately affect their intention to reuse chatbot/IM. The findings reveal determinants of chatbot/IM adoption which have rarely been mentioned by previous work.

Practitioners should note that consumers in general still prefer to interact with human conversational agents. Practitioners should contemplate how to combine chatbot and human resources effectively to deliver the best customer service.

This study goes beyond the Computer as Social Actor paradigm and Technology Acceptance Model to understand chatbot and IM adoption. It is among one of the first studies that compare chatbot and IM use experience in the tourism and hospitality literature.

As computer‐mediated communication has diffused, successive technological variations raise new questions about interpersonal impressions and several standardized instruments have been advanced in literature to asses various aspect of interpersonal attraction phenomena. The purpose of this paper is to examine the claims for reliability and validity of the attraction scales by using confirmatory factor analysis (CFA) in the light of research since 1974 which has employed the scales, and then improve the measures if needed.

Random sampling procedure was carried out in two kulliyahs (Faculty) at International Islamic university Malaysia (IIUM), in which 340 students were selected from a population of 2,000 undergraduate students. A total of 26 items were tested from an instrument that has been used in previous studies; each item is expected to measure one of the three orientations' dimensions.

The analysis result confirms that the orientation of students towards interpersonal communication in online social network sites (SNS) is a multi‐dimensional construct consisting of social attraction, physical attraction and task attraction. In addition, since substantial numbers of research studies have been reported to have employed one or more of these measures, this research was examined to evaluate their reliability and validity.

Since the reliabilities found in literatures have been highly varied, it was determined that improved measures should be used in the future. Thus, a revised and improved version of these measures, using CFA, are reported and recommended in this paper for future research.

The paper has produced a second‐generation measure with substantially improved internal reliability and validity. The paper's most important and obvious conclusion is that interpersonal attraction does appear to be a multidimensional construct.

Teams are best positioned for success when certain enabling conditions are in place such as the right mix of individuals. Effective team staffing considers team members’ knowledge, skills, abilities, and other characteristics (KSAOs) as well as the configuration of team member KSAOs and their relations, called team composition. In practice, however, how to integrate team composition considerations into team staffing to facilitate outcomes such as team cohesion can seem nebulous. The purpose of this chapter is to describe how team member KSAOs and their configurations and relations affect team cohesion, and suggest how this information can inform team staffing. We frame team cohesion as an aspect of team human capital to understand when it may be an important consideration for staffing. We describe multilevel considerations in staffing cohesive teams. We summarize theories that link team composition to team cohesion via interpersonal attraction, a shared team identity, and team task commitment. Finally, we propose a six-step approach for staffing cohesive teams, and describe a few areas for future research.

Cohesion is a key contributor to team effectiveness, leading to great interest in understanding how to diagnose, monitor, and enhance it in practice. However, there is great inconsistency in how cohesion is conceptualized and measured, making it difficult to compare findings across studies, and therefore limiting the ability to advance science and practice. To begin addressing these issues, we draw from qualitative and quantitative analyses and extract themes indicating what matters most for effective cohesion measurement. Such themes are presented around six major questions – who, what, when, where, why, and how – as they pertain to each major component of the cohesion measurement process. Emerging approaches to cohesion measurement and corresponding avenues for future research are also discussed.

This chapter reviews the challenges associated with measuring and studying cohesion over time and provides guidance for addressing these issues in future research.

We reviewed the team cohesion and team development literatures, including definitions and conceptualizations of cohesion as well as the seminal team development taxonomies. We then integrated these literatures to identify the challenges and potential solutions for studying team cohesion as a dynamic construct.

We identified five key challenges – theoretical and practical in nature – that researchers and organizations often face in capturing and studying team cohesion emergence: problems with self-report measures; measuring multiple dimensions of cohesion at appropriate times; failure to combine multilevel and temporal frameworks; and tracking of team and organizational events. In response, we provide actions that researchers can take in addressing these challenges: using indirect/unobtrusive measures; using social network analysis; studying “swift cohesion”; adopting an event system theory framework; and applying agent-based modeling.

This comprehensive chapter provides recommendations for studying team cohesion as a dynamic, emergent process rather than as a static state. We discuss the challenges pertaining to study design and measurement when capturing team cohesion emergence, and provide theoretical and practical ideas to guide researchers in overcoming these issues in future research.

This chapter suggests tools and data collection techniques that organizations and practitioners can use for measuring and improving team cohesion, such as using unobtrusive measures and timing measurement according to team and organizational events.

Whereas social cohesion has been widely studied and researched by sociologist and psychologists, its application to public procurement is sparse. This study explores the connection between social cohesion, groupthink, ethical attitudes and ethical behavior of procurement officers. The study is based on a survey of 405 public procurement officers in central government. A cross-sectional survey design was used and a response rate of 58.5% attained. Self report items were used to study all the constructs. All the hypothesized relationships were found to be significant. Social cohesion, groupthink, and ethical attitudes were all significant predictors of ethical behavior, accounting for 56% of the variance. The strength of this prediction suggests the need for concerted policy intervention for dealing with unethical conduct and behavior of the procurement professionals.

Based on the post-acceptance model of information system continuance (PAMISC), this study investigates the influence of the early-stage users' personal traits (specifically personal innovativeness and technology anxiety) and ex-post instrumentality perceptions (specifically price value, hedonic motivation, compatibility and perceived security) on social diffusion of smart technologies measured by the intention to recommend artificial intelligence-based voice assistant systems (AIVAS) to others.

Survey data from 400 US AIVAS users were collected and analyzed with Statistical Product and Service Solutions (SPSS) 18.0 and the partial least square technique using advanced analysis of composites (ADANCO) 2.1.

AIVAS technology is presently at the early stage of market penetration (about 25% of market penetration in the USA). A survey of AIVAS technology users reveals that personal innovativeness is directly and indirectly (through confirmation and continuance) associated with a stronger intention to recommend the use of the device to others. Confirmation is associated with all four ex-post instrumentality perceptions (hedonic motivation, compatibility, price value and perceived security). Among the four, however, only hedonic motivation and compatibility are significant predictors of satisfaction, which lead to use continuance and, eventually, intention to recommend. Finally, technology anxiety is found to be indirectly (but not directly) associated with a lower intention to recommend.

This is the first study conducted on the early-stage AIVAS users that evaluates the influence of both personal traits and ex-post instrumentality perceptions on users' intention for continuance and recommendation to others.